Overview

Brought to you by YData

Dataset statistics

Number of variables39
Number of observations19965
Missing cells21602
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.9 MiB
Average record size in memory1.9 KiB

Variable types

Categorical9
Numeric17
Text12
Unsupported1

Alerts

a_minus is highly overall correlated with b_minus and 1 other fieldsHigh correlation
average_grade is highly overall correlated with b and 3 other fieldsHigh correlation
b is highly overall correlated with average_grade and 1 other fieldsHigh correlation
b_minus is highly overall correlated with a_minus and 4 other fieldsHigh correlation
b_plus is highly overall correlated with a_minus and 2 other fieldsHigh correlation
c is highly overall correlated with average_grade and 2 other fieldsHigh correlation
c_minus is highly overall correlated with b_minus and 1 other fieldsHigh correlation
c_plus is highly overall correlated with average_grade and 3 other fieldsHigh correlation
d is highly overall correlated with cHigh correlation
part_of_term is highly imbalanced (66.6%) Imbalance
section_credit_hours is highly imbalanced (62.8%) Imbalance
section_status is highly imbalanced (99.7%) Imbalance
subject_section has 982 (4.9%) missing values Missing
part_of_term has 655 (3.3%) missing values Missing
credit_hours has 19965 (100.0%) missing values Missing
credit_hours is an unsupported type, check if it needs cleaning or further analysis Unsupported
a_plus has 5025 (25.2%) zeros Zeros
a_minus has 2912 (14.6%) zeros Zeros
b_plus has 3848 (19.3%) zeros Zeros
b has 3085 (15.5%) zeros Zeros
b_minus has 7973 (39.9%) zeros Zeros
c_plus has 10415 (52.2%) zeros Zeros
c has 9405 (47.1%) zeros Zeros
c_minus has 13268 (66.5%) zeros Zeros
d_plus has 15822 (79.2%) zeros Zeros
d has 14264 (71.4%) zeros Zeros
d_minus has 16769 (84.0%) zeros Zeros
f has 11269 (56.4%) zeros Zeros
w has 17651 (88.4%) zeros Zeros

Reproduction

Analysis started2025-05-12 02:00:57.206652
Analysis finished2025-05-12 02:01:40.293454
Duration43.09 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2022
5178 
2023
5134 
2021
5090 
2020
4563 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters79860
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2022 5178
25.9%
2023 5134
25.7%
2021 5090
25.5%
2020 4563
22.9%

Length

2025-05-12T02:01:40.395190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T02:01:40.482547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2022 5178
25.9%
2023 5134
25.7%
2021 5090
25.5%
2020 4563
22.9%

Most occurring characters

ValueCountFrequency (%)
2 45108
56.5%
0 24528
30.7%
3 5134
 
6.4%
1 5090
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 45108
56.5%
0 24528
30.7%
3 5134
 
6.4%
1 5090
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 45108
56.5%
0 24528
30.7%
3 5134
 
6.4%
1 5090
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 45108
56.5%
0 24528
30.7%
3 5134
 
6.4%
1 5090
 
6.4%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
FALL
10896 
SPRING
9069 

Length

Max length6
Median length4
Mean length4.9084899
Min length4

Characters and Unicode

Total characters97998
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFALL
2nd rowFALL
3rd rowFALL
4th rowFALL
5th rowFALL

Common Values

ValueCountFrequency (%)
FALL 10896
54.6%
SPRING 9069
45.4%

Length

2025-05-12T02:01:40.587179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T02:01:40.662303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fall 10896
54.6%
spring 9069
45.4%

Most occurring characters

ValueCountFrequency (%)
L 21792
22.2%
F 10896
11.1%
A 10896
11.1%
S 9069
9.3%
P 9069
9.3%
R 9069
9.3%
I 9069
9.3%
N 9069
9.3%
G 9069
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 21792
22.2%
F 10896
11.1%
A 10896
11.1%
S 9069
9.3%
P 9069
9.3%
R 9069
9.3%
I 9069
9.3%
N 9069
9.3%
G 9069
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 21792
22.2%
F 10896
11.1%
A 10896
11.1%
S 9069
9.3%
P 9069
9.3%
R 9069
9.3%
I 9069
9.3%
N 9069
9.3%
G 9069
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 21792
22.2%
F 10896
11.1%
A 10896
11.1%
S 9069
9.3%
P 9069
9.3%
R 9069
9.3%
I 9069
9.3%
N 9069
9.3%
G 9069
9.3%

crn
Real number (ℝ)

Distinct8096
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53458.682
Minimum10051
Maximum78820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:40.757959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10051
5-th percentile31044.4
Q137449
median55464
Q367938
95-th percentile74665.6
Maximum78820
Range68769
Interquartile range (IQR)30489

Descriptive statistics

Standard deviation15376.586
Coefficient of variation (CV)0.28763497
Kurtosis-1.4422438
Mean53458.682
Median Absolute Deviation (MAD)14793
Skewness-0.11410167
Sum1.0673026 × 109
Variance2.3643941 × 108
MonotonicityNot monotonic
2025-05-12T02:01:40.896938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53068 28
 
0.1%
53066 24
 
0.1%
55158 22
 
0.1%
65138 13
 
0.1%
56271 12
 
0.1%
75873 12
 
0.1%
35803 12
 
0.1%
35802 11
 
0.1%
61888 10
 
0.1%
29863 10
 
0.1%
Other values (8086) 19811
99.2%
ValueCountFrequency (%)
10051 2
< 0.1%
11484 1
 
< 0.1%
15375 1
 
< 0.1%
20680 1
 
< 0.1%
22099 2
< 0.1%
27106 1
 
< 0.1%
27137 1
 
< 0.1%
29649 4
< 0.1%
29650 2
< 0.1%
29656 4
< 0.1%
ValueCountFrequency (%)
78820 1
< 0.1%
78770 1
< 0.1%
78742 1
< 0.1%
78664 1
< 0.1%
78662 1
< 0.1%
78661 1
< 0.1%
78660 1
< 0.1%
78644 2
< 0.1%
78636 1
< 0.1%
78635 1
< 0.1%

subject_section
Text

Missing 

Distinct1458
Distinct (%)7.7%
Missing982
Missing (%)4.9%
Memory size1.1 MiB
2025-05-12T02:01:41.292171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0909235
Min length1

Characters and Unicode

Total characters39692
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)2.5%

Sample

1st rowAD1
2nd rowAD2
3rd rowAD3
4th rowAD4
5th rowAD5
ValueCountFrequency (%)
a 3614
 
19.0%
al1 1490
 
7.8%
b 882
 
4.6%
c 447
 
2.4%
onl 365
 
1.9%
d 321
 
1.7%
e 247
 
1.3%
f 187
 
1.0%
ad1 186
 
1.0%
al2 183
 
1.0%
Other values (1444) 11061
58.3%
2025-05-12T02:01:41.779701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 9871
24.9%
1 4170
10.5%
L 3375
 
8.5%
D 3369
 
8.5%
B 2317
 
5.8%
3 1480
 
3.7%
O 1471
 
3.7%
2 1412
 
3.6%
C 1402
 
3.5%
E 1314
 
3.3%
Other values (28) 9511
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 9871
24.9%
1 4170
10.5%
L 3375
 
8.5%
D 3369
 
8.5%
B 2317
 
5.8%
3 1480
 
3.7%
O 1471
 
3.7%
2 1412
 
3.6%
C 1402
 
3.5%
E 1314
 
3.3%
Other values (28) 9511
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 9871
24.9%
1 4170
10.5%
L 3375
 
8.5%
D 3369
 
8.5%
B 2317
 
5.8%
3 1480
 
3.7%
O 1471
 
3.7%
2 1412
 
3.6%
C 1402
 
3.5%
E 1314
 
3.3%
Other values (28) 9511
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 9871
24.9%
1 4170
10.5%
L 3375
 
8.5%
D 3369
 
8.5%
B 2317
 
5.8%
3 1480
 
3.7%
O 1471
 
3.7%
2 1412
 
3.6%
C 1402
 
3.5%
E 1314
 
3.3%
Other values (28) 9511
24.0%

course_number
Real number (ℝ)

Distinct508
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.285
Minimum100
Maximum798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:41.909836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1189
median301
Q3435
95-th percentile571
Maximum798
Range698
Interquartile range (IQR)246

Descriptive statistics

Standard deviation155.95698
Coefficient of variation (CV)0.50753202
Kurtosis-0.98905205
Mean307.285
Median Absolute Deviation (MAD)131
Skewness0.25160471
Sum6134945
Variance24322.578
MonotonicityNot monotonic
2025-05-12T02:01:42.045985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1211
 
6.1%
101 1185
 
5.9%
102 361
 
1.8%
201 320
 
1.6%
103 285
 
1.4%
150 248
 
1.2%
250 245
 
1.2%
202 221
 
1.1%
199 216
 
1.1%
104 213
 
1.1%
Other values (498) 15460
77.4%
ValueCountFrequency (%)
100 1211
6.1%
101 1185
5.9%
102 361
 
1.8%
103 285
 
1.4%
104 213
 
1.1%
105 78
 
0.4%
106 16
 
0.1%
107 31
 
0.2%
108 21
 
0.1%
109 37
 
0.2%
ValueCountFrequency (%)
798 1
 
< 0.1%
797 6
 
< 0.1%
796 3
 
< 0.1%
795 2
 
< 0.1%
794 8
< 0.1%
792 18
0.1%
694 4
 
< 0.1%
686 3
 
< 0.1%
684 3
 
< 0.1%
682 7
 
< 0.1%
Distinct3456
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-05-12T02:01:42.343339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length25
Mean length23.606061
Min length3

Characters and Unicode

Total characters471295
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique953 ?
Unique (%)4.8%

Sample

1st rowIntro Asian American Studies
2nd rowIntro Asian American Studies
3rd rowIntro Asian American Studies
4th rowIntro Asian American Studies
5th rowIntro Asian American Studies
ValueCountFrequency (%)
2449
 
3.7%
and 2150
 
3.2%
to 2072
 
3.1%
intro 1671
 
2.5%
of 1604
 
2.4%
in 1468
 
2.2%
i 901
 
1.3%
introduction 898
 
1.3%
design 768
 
1.1%
ii 742
 
1.1%
Other values (2635) 52346
78.0%
2025-05-12T02:01:42.747755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47104
 
10.0%
i 36695
 
7.8%
n 35737
 
7.6%
e 35011
 
7.4%
o 31644
 
6.7%
t 30626
 
6.5%
a 28776
 
6.1%
r 24953
 
5.3%
s 23223
 
4.9%
c 20417
 
4.3%
Other values (66) 157109
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 471295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47104
 
10.0%
i 36695
 
7.8%
n 35737
 
7.6%
e 35011
 
7.4%
o 31644
 
6.7%
t 30626
 
6.5%
a 28776
 
6.1%
r 24953
 
5.3%
s 23223
 
4.9%
c 20417
 
4.3%
Other values (66) 157109
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 471295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47104
 
10.0%
i 36695
 
7.8%
n 35737
 
7.6%
e 35011
 
7.4%
o 31644
 
6.7%
t 30626
 
6.5%
a 28776
 
6.1%
r 24953
 
5.3%
s 23223
 
4.9%
c 20417
 
4.3%
Other values (66) 157109
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 471295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47104
 
10.0%
i 36695
 
7.8%
n 35737
 
7.6%
e 35011
 
7.4%
o 31644
 
6.7%
t 30626
 
6.5%
a 28776
 
6.1%
r 24953
 
5.3%
s 23223
 
4.9%
c 20417
 
4.3%
Other values (66) 157109
33.3%
Distinct3510
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2025-05-12T02:01:43.093524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1858
Median length832
Mean length423.23857
Min length16

Characters and Unicode

Total characters8449958
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique925 ?
Unique (%)4.6%

Sample

1st rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
2nd rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
3rd rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
4th rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
5th rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
ValueCountFrequency (%)
and 83775
 
7.0%
of 55381
 
4.6%
the 45829
 
3.8%
to 26193
 
2.2%
in 24918
 
2.1%
for 16296
 
1.4%
or 14568
 
1.2%
a 12263
 
1.0%
hours 11970
 
1.0%
prerequisite 11077
 
0.9%
Other values (12276) 899069
74.8%
2025-05-12T02:01:43.629965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1181791
14.0%
e 748791
 
8.9%
i 587778
 
7.0%
n 561843
 
6.6%
t 552731
 
6.5%
a 536700
 
6.4%
o 520131
 
6.2%
s 488164
 
5.8%
r 465437
 
5.5%
c 290969
 
3.4%
Other values (85) 2515623
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8449958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1181791
14.0%
e 748791
 
8.9%
i 587778
 
7.0%
n 561843
 
6.6%
t 552731
 
6.5%
a 536700
 
6.4%
o 520131
 
6.2%
s 488164
 
5.8%
r 465437
 
5.5%
c 290969
 
3.4%
Other values (85) 2515623
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8449958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1181791
14.0%
e 748791
 
8.9%
i 587778
 
7.0%
n 561843
 
6.6%
t 552731
 
6.5%
a 536700
 
6.4%
o 520131
 
6.2%
s 488164
 
5.8%
r 465437
 
5.5%
c 290969
 
3.4%
Other values (85) 2515623
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8449958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1181791
14.0%
e 748791
 
8.9%
i 587778
 
7.0%
n 561843
 
6.6%
t 552731
 
6.5%
a 536700
 
6.4%
o 520131
 
6.2%
s 488164
 
5.8%
r 465437
 
5.5%
c 290969
 
3.4%
Other values (85) 2515623
29.8%

part_of_term
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing655
Missing (%)3.3%
Memory size1.1 MiB
1.0
18120 
2.0
 
1190

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57930
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 18120
90.8%
2.0 1190
 
6.0%
(Missing) 655
 
3.3%

Length

2025-05-12T02:01:43.740190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T02:01:43.806719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 18120
93.8%
2.0 1190
 
6.2%

Most occurring characters

ValueCountFrequency (%)
. 19310
33.3%
0 19310
33.3%
1 18120
31.3%
2 1190
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 19310
33.3%
0 19310
33.3%
1 18120
31.3%
2 1190
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 19310
33.3%
0 19310
33.3%
1 18120
31.3%
2 1190
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 19310
33.3%
0 19310
33.3%
1 18120
31.3%
2 1190
 
2.1%
Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-05-12T02:01:43.942389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length99
Median length7
Mean length16.673328
Min length7

Characters and Unicode

Total characters332883
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
2nd rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
3rd rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
4th rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
5th rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
ValueCountFrequency (%)
unknown 14922
26.8%
9070
16.3%
course 5043
 
9.0%
sci 3465
 
6.2%
studies 1850
 
3.3%
cultural 1850
 
3.3%
and 1845
 
3.3%
beh 1809
 
3.2%
humanities 1594
 
2.9%
social 1449
 
2.6%
Other values (29) 12858
23.1%
2025-05-12T02:01:44.909259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 54319
16.3%
35790
 
10.8%
o 26543
 
8.0%
i 18223
 
5.5%
e 16246
 
4.9%
U 15662
 
4.7%
k 14922
 
4.5%
w 14922
 
4.5%
s 13582
 
4.1%
c 13424
 
4.0%
Other values (36) 109250
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 332883
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 54319
16.3%
35790
 
10.8%
o 26543
 
8.0%
i 18223
 
5.5%
e 16246
 
4.9%
U 15662
 
4.7%
k 14922
 
4.5%
w 14922
 
4.5%
s 13582
 
4.1%
c 13424
 
4.0%
Other values (36) 109250
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 332883
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 54319
16.3%
35790
 
10.8%
o 26543
 
8.0%
i 18223
 
5.5%
e 16246
 
4.9%
U 15662
 
4.7%
k 14922
 
4.5%
w 14922
 
4.5%
s 13582
 
4.1%
c 13424
 
4.0%
Other values (36) 109250
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 332883
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 54319
16.3%
35790
 
10.8%
o 26543
 
8.0%
i 18223
 
5.5%
e 16246
 
4.9%
U 15662
 
4.7%
k 14922
 
4.5%
w 14922
 
4.5%
s 13582
 
4.1%
c 13424
 
4.0%
Other values (36) 109250
32.8%

credit_hours
Unsupported

Missing  Rejected  Unsupported 

Missing19965
Missing (%)100.0%
Memory size156.1 KiB

section_credit_hours
Categorical

Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Unknown
15394 
3 hours
2211 
4 hours
 
1338
1 hours
 
514
2 hours
 
488
Other values (4)
 
20

Length

Max length9
Median length7
Mean length7.0008515
Min length7

Characters and Unicode

Total characters139772
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 15394
77.1%
3 hours 2211
 
11.1%
4 hours 1338
 
6.7%
1 hours 514
 
2.6%
2 hours 488
 
2.4%
5 hours 9
 
< 0.1%
4.5 hours 8
 
< 0.1%
8 hours 2
 
< 0.1%
12 hours 1
 
< 0.1%

Length

2025-05-12T02:01:45.020652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T02:01:45.114049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown 15394
62.7%
hours 4571
 
18.6%
3 2211
 
9.0%
4 1338
 
5.5%
1 514
 
2.1%
2 488
 
2.0%
5 9
 
< 0.1%
4.5 8
 
< 0.1%
8 2
 
< 0.1%
12 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 46182
33.0%
o 19965
14.3%
U 15394
 
11.0%
k 15394
 
11.0%
w 15394
 
11.0%
4571
 
3.3%
h 4571
 
3.3%
u 4571
 
3.3%
r 4571
 
3.3%
s 4571
 
3.3%
Other values (7) 4588
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 46182
33.0%
o 19965
14.3%
U 15394
 
11.0%
k 15394
 
11.0%
w 15394
 
11.0%
4571
 
3.3%
h 4571
 
3.3%
u 4571
 
3.3%
r 4571
 
3.3%
s 4571
 
3.3%
Other values (7) 4588
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 46182
33.0%
o 19965
14.3%
U 15394
 
11.0%
k 15394
 
11.0%
w 15394
 
11.0%
4571
 
3.3%
h 4571
 
3.3%
u 4571
 
3.3%
r 4571
 
3.3%
s 4571
 
3.3%
Other values (7) 4588
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 46182
33.0%
o 19965
14.3%
U 15394
 
11.0%
k 15394
 
11.0%
w 15394
 
11.0%
4571
 
3.3%
h 4571
 
3.3%
u 4571
 
3.3%
r 4571
 
3.3%
s 4571
 
3.3%
Other values (7) 4588
 
3.3%

section_status
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
A
19961 
P
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19965
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 19961
> 99.9%
P 4
 
< 0.1%

Length

2025-05-12T02:01:45.233521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T02:01:45.293455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 19961
> 99.9%
p 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 19961
> 99.9%
P 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 19961
> 99.9%
P 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 19961
> 99.9%
P 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 19961
> 99.9%
P 4
 
< 0.1%

section_type
Categorical

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Lecture-Discussion
6564 
Online
4549 
Lecture
2883 
Discussion/Recitation
2179 
Online Lecture
1671 
Other values (13)
2119 

Length

Max length25
Median length21
Mean length13.198598
Min length4

Characters and Unicode

Total characters263510
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOnline Discussion
2nd rowOnline Discussion
3rd rowOnline Discussion
4th rowOnline Discussion
5th rowOnline Discussion

Common Values

ValueCountFrequency (%)
Lecture-Discussion 6564
32.9%
Online 4549
22.8%
Lecture 2883
14.4%
Discussion/Recitation 2179
 
10.9%
Online Lecture 1671
 
8.4%
Online Discussion 827
 
4.1%
Laboratory 760
 
3.8%
Online Lab 208
 
1.0%
Laboratory-Discussion 140
 
0.7%
Practice 64
 
0.3%
Other values (8) 120
 
0.6%

Length

2025-05-12T02:01:45.402227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
online 7285
32.0%
lecture-discussion 6564
28.9%
lecture 4584
20.2%
discussion/recitation 2179
 
9.6%
discussion 857
 
3.8%
laboratory 760
 
3.3%
lab 208
 
0.9%
laboratory-discussion 140
 
0.6%
practice 64
 
0.3%
studio 34
 
0.1%
Other values (8) 69
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 31935
12.1%
i 31253
11.9%
s 29227
11.1%
n 26572
10.1%
c 23242
8.8%
u 20928
7.9%
t 16525
 
6.3%
o 13788
 
5.2%
r 13050
 
5.0%
L 12256
 
4.7%
Other values (23) 44734
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 263510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 31935
12.1%
i 31253
11.9%
s 29227
11.1%
n 26572
10.1%
c 23242
8.8%
u 20928
7.9%
t 16525
 
6.3%
o 13788
 
5.2%
r 13050
 
5.0%
L 12256
 
4.7%
Other values (23) 44734
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 263510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 31935
12.1%
i 31253
11.9%
s 29227
11.1%
n 26572
10.1%
c 23242
8.8%
u 20928
7.9%
t 16525
 
6.3%
o 13788
 
5.2%
r 13050
 
5.0%
L 12256
 
4.7%
Other values (23) 44734
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 263510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 31935
12.1%
i 31253
11.9%
s 29227
11.1%
n 26572
10.1%
c 23242
8.8%
u 20928
7.9%
t 16525
 
6.3%
o 13788
 
5.2%
r 13050
 
5.0%
L 12256
 
4.7%
Other values (23) 44734
17.0%
Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-05-12T02:01:45.572603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9324818
Min length1

Characters and Unicode

Total characters58547
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st rowOD
2nd rowOD
3rd rowOD
4th rowOD
5th rowOD
ValueCountFrequency (%)
lcd 6365
31.9%
onl 4363
21.9%
lec 2851
14.3%
dis 2178
 
10.9%
olc 1671
 
8.4%
od 827
 
4.1%
lab 706
 
3.5%
olb 208
 
1.0%
lbd 140
 
0.7%
e1 70
 
0.4%
Other values (61) 586
 
2.9%
2025-05-12T02:01:45.850910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 16366
28.0%
C 10910
18.6%
D 9542
16.3%
O 7099
12.1%
N 4389
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 16366
28.0%
C 10910
18.6%
D 9542
16.3%
O 7099
12.1%
N 4389
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 16366
28.0%
C 10910
18.6%
D 9542
16.3%
O 7099
12.1%
N 4389
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 16366
28.0%
C 10910
18.6%
D 9542
16.3%
O 7099
12.1%
N 4389
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
UNKNOWN
5009 
Open (Restricted)
4390 
Open
4224 
Closed
4084 
CrossListOpen (Restricted)
1286 

Length

Max length26
Median length13
Mean length9.8755322
Min length4

Characters and Unicode

Total characters197165
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen
2nd rowOpen
3rd rowOpen
4th rowOpen
5th rowClosed

Common Values

ValueCountFrequency (%)
UNKNOWN 5009
25.1%
Open (Restricted) 4390
22.0%
Open 4224
21.2%
Closed 4084
20.5%
CrossListOpen (Restricted) 1286
 
6.4%
CrossListOpen 972
 
4.9%

Length

2025-05-12T02:01:45.952050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T02:01:46.036640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
open 8614
33.6%
restricted 5676
22.1%
unknown 5009
19.5%
closed 4084
15.9%
crosslistopen 2258
 
8.8%

Most occurring characters

ValueCountFrequency (%)
e 26308
13.3%
s 16534
 
8.4%
O 15881
 
8.1%
N 15027
 
7.6%
t 13610
 
6.9%
p 10872
 
5.5%
n 10872
 
5.5%
d 9760
 
5.0%
i 7934
 
4.0%
r 7934
 
4.0%
Other values (12) 62433
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 26308
13.3%
s 16534
 
8.4%
O 15881
 
8.1%
N 15027
 
7.6%
t 13610
 
6.9%
p 10872
 
5.5%
n 10872
 
5.5%
d 9760
 
5.0%
i 7934
 
4.0%
r 7934
 
4.0%
Other values (12) 62433
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 26308
13.3%
s 16534
 
8.4%
O 15881
 
8.1%
N 15027
 
7.6%
t 13610
 
6.9%
p 10872
 
5.5%
n 10872
 
5.5%
d 9760
 
5.0%
i 7934
 
4.0%
r 7934
 
4.0%
Other values (12) 62433
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 26308
13.3%
s 16534
 
8.4%
O 15881
 
8.1%
N 15027
 
7.6%
t 13610
 
6.9%
p 10872
 
5.5%
n 10872
 
5.5%
d 9760
 
5.0%
i 7934
 
4.0%
r 7934
 
4.0%
Other values (12) 62433
31.7%

start_time
Categorical

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
11:00 AM
2935 
ARRANGED
2900 
02:00 PM
2328 
01:00 PM
1558 
10:00 AM
1352 
Other values (31)
8892 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters159720
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row09:00 AM
2nd row12:00 PM
3rd row01:00 PM
4th row10:00 AM
5th row11:00 AM

Common Values

ValueCountFrequency (%)
11:00 AM 2935
14.7%
ARRANGED 2900
14.5%
02:00 PM 2328
11.7%
01:00 PM 1558
7.8%
10:00 AM 1352
6.8%
09:30 AM 1347
6.7%
09:00 AM 1261
 
6.3%
12:30 PM 1206
 
6.0%
12:00 PM 961
 
4.8%
03:30 PM 866
 
4.3%
Other values (26) 3251
16.3%

Length

2025-05-12T02:01:46.143399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm 9514
25.7%
am 7551
20.4%
11:00 2935
 
7.9%
arranged 2900
 
7.8%
02:00 2328
 
6.3%
01:00 1558
 
4.2%
10:00 1353
 
3.7%
09:30 1347
 
3.6%
09:00 1262
 
3.4%
12:30 1206
 
3.3%
Other values (24) 5076
13.7%

Most occurring characters

ValueCountFrequency (%)
0 42111
26.4%
: 17065
10.7%
17065
10.7%
M 17065
10.7%
A 13351
 
8.4%
1 11195
 
7.0%
P 9514
 
6.0%
R 5800
 
3.6%
3 5682
 
3.6%
2 4543
 
2.8%
Other values (10) 16329
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 42111
26.4%
: 17065
10.7%
17065
10.7%
M 17065
10.7%
A 13351
 
8.4%
1 11195
 
7.0%
P 9514
 
6.0%
R 5800
 
3.6%
3 5682
 
3.6%
2 4543
 
2.8%
Other values (10) 16329
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 42111
26.4%
: 17065
10.7%
17065
10.7%
M 17065
10.7%
A 13351
 
8.4%
1 11195
 
7.0%
P 9514
 
6.0%
R 5800
 
3.6%
3 5682
 
3.6%
2 4543
 
2.8%
Other values (10) 16329
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 42111
26.4%
: 17065
10.7%
17065
10.7%
M 17065
10.7%
A 13351
 
8.4%
1 11195
 
7.0%
P 9514
 
6.0%
R 5800
 
3.6%
3 5682
 
3.6%
2 4543
 
2.8%
Other values (10) 16329
 
10.2%
Distinct109
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-05-12T02:01:46.357547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.8547458
Min length7

Characters and Unicode

Total characters156820
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row09:50 AM
2nd row12:50 PM
3rd row01:50 PM
4th row10:50 AM
5th row11:50 AM
ValueCountFrequency (%)
pm 11285
30.5%
am 5780
15.6%
unknown 2900
 
7.8%
10:50 2409
 
6.5%
01:50 2147
 
5.8%
11:50 1669
 
4.5%
12:20 1269
 
3.4%
04:50 1230
 
3.3%
03:20 1126
 
3.0%
02:50 1061
 
2.9%
Other values (93) 6154
16.6%
2025-05-12T02:01:46.698591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28897
18.4%
17065
10.9%
: 17065
10.9%
M 17065
10.9%
5 13393
8.5%
1 11844
7.6%
P 11285
 
7.2%
n 8700
 
5.5%
2 7412
 
4.7%
A 5780
 
3.7%
Other values (10) 18314
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28897
18.4%
17065
10.9%
: 17065
10.9%
M 17065
10.9%
5 13393
8.5%
1 11844
7.6%
P 11285
 
7.2%
n 8700
 
5.5%
2 7412
 
4.7%
A 5780
 
3.7%
Other values (10) 18314
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28897
18.4%
17065
10.9%
: 17065
10.9%
M 17065
10.9%
5 13393
8.5%
1 11844
7.6%
P 11285
 
7.2%
n 8700
 
5.5%
2 7412
 
4.7%
A 5780
 
3.7%
Other values (10) 18314
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28897
18.4%
17065
10.9%
: 17065
10.9%
M 17065
10.9%
5 13393
8.5%
1 11844
7.6%
P 11285
 
7.2%
n 8700
 
5.5%
2 7412
 
4.7%
A 5780
 
3.7%
Other values (10) 18314
11.7%

days_of_week
Categorical

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
TR
5161 
MW
2996 
Unknown
2855 
MWF
2582 
F
1792 
Other values (24)
4579 

Length

Max length7
Median length5
Mean length2.5621838
Min length1

Characters and Unicode

Total characters51154
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
TR 5161
25.9%
MW 2996
15.0%
Unknown 2855
14.3%
MWF 2582
12.9%
F 1792
 
9.0%
R 1207
 
6.0%
W 1131
 
5.7%
T 962
 
4.8%
M 805
 
4.0%
WF 111
 
0.6%
Other values (19) 363
 
1.8%

Length

2025-05-12T02:01:46.811471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tr 5161
25.9%
mw 2996
15.0%
unknown 2855
14.3%
mwf 2582
12.9%
f 1792
 
9.0%
r 1207
 
6.0%
w 1131
 
5.7%
t 962
 
4.8%
m 805
 
4.0%
wf 111
 
0.6%
Other values (19) 363
 
1.8%

Most occurring characters

ValueCountFrequency (%)
n 8565
16.7%
W 7037
13.8%
M 6620
12.9%
R 6508
12.7%
T 6349
12.4%
F 4615
9.0%
U 2857
 
5.6%
k 2855
 
5.6%
o 2855
 
5.6%
w 2855
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 8565
16.7%
W 7037
13.8%
M 6620
12.9%
R 6508
12.7%
T 6349
12.4%
F 4615
9.0%
U 2857
 
5.6%
k 2855
 
5.6%
o 2855
 
5.6%
w 2855
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 8565
16.7%
W 7037
13.8%
M 6620
12.9%
R 6508
12.7%
T 6349
12.4%
F 4615
9.0%
U 2857
 
5.6%
k 2855
 
5.6%
o 2855
 
5.6%
w 2855
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 8565
16.7%
W 7037
13.8%
M 6620
12.9%
R 6508
12.7%
T 6349
12.4%
F 4615
9.0%
U 2857
 
5.6%
k 2855
 
5.6%
o 2855
 
5.6%
w 2855
 
5.6%

room
Text

Distinct427
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-05-12T02:01:47.192916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.7500626
Min length1

Characters and Unicode

Total characters94835
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.2%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown 7667
38.4%
100 239
 
1.2%
103 192
 
1.0%
1002 171
 
0.9%
215 166
 
0.8%
141 159
 
0.8%
111 149
 
0.7%
112 138
 
0.7%
166 131
 
0.7%
2001 131
 
0.7%
Other values (418) 10826
54.2%
2025-05-12T02:01:48.077562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 23001
24.3%
1 10065
10.6%
0 7763
 
8.2%
U 7727
 
8.1%
k 7667
 
8.1%
w 7667
 
8.1%
o 7667
 
8.1%
2 6322
 
6.7%
3 5398
 
5.7%
4 2751
 
2.9%
Other values (29) 8807
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 23001
24.3%
1 10065
10.6%
0 7763
 
8.2%
U 7727
 
8.1%
k 7667
 
8.1%
w 7667
 
8.1%
o 7667
 
8.1%
2 6322
 
6.7%
3 5398
 
5.7%
4 2751
 
2.9%
Other values (29) 8807
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 23001
24.3%
1 10065
10.6%
0 7763
 
8.2%
U 7727
 
8.1%
k 7667
 
8.1%
w 7667
 
8.1%
o 7667
 
8.1%
2 6322
 
6.7%
3 5398
 
5.7%
4 2751
 
2.9%
Other values (29) 8807
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 23001
24.3%
1 10065
10.6%
0 7763
 
8.2%
U 7727
 
8.1%
k 7667
 
8.1%
w 7667
 
8.1%
o 7667
 
8.1%
2 6322
 
6.7%
3 5398
 
5.7%
4 2751
 
2.9%
Other values (29) 8807
 
9.3%
Distinct99
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-05-12T02:01:48.926753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length29
Mean length13.909992
Min length6

Characters and Unicode

Total characters277713
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown 7667
19.1%
hall 4943
 
12.3%
building 1766
 
4.4%
instructional 1659
 
4.1%
business 1189
 
3.0%
fac 1189
 
3.0%
laboratory 1079
 
2.7%
bldg 947
 
2.4%
gregory 845
 
2.1%
843
 
2.1%
Other values (169) 17916
44.7%
2025-05-12T02:01:49.521210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 37697
 
13.6%
l 21828
 
7.9%
20150
 
7.3%
o 18838
 
6.8%
i 16766
 
6.0%
a 15987
 
5.8%
r 14804
 
5.3%
e 11897
 
4.3%
t 9880
 
3.6%
s 9385
 
3.4%
Other values (48) 100481
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 277713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 37697
 
13.6%
l 21828
 
7.9%
20150
 
7.3%
o 18838
 
6.8%
i 16766
 
6.0%
a 15987
 
5.8%
r 14804
 
5.3%
e 11897
 
4.3%
t 9880
 
3.6%
s 9385
 
3.4%
Other values (48) 100481
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 277713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 37697
 
13.6%
l 21828
 
7.9%
20150
 
7.3%
o 18838
 
6.8%
i 16766
 
6.0%
a 15987
 
5.8%
r 14804
 
5.3%
e 11897
 
4.3%
t 9880
 
3.6%
s 9385
 
3.4%
Other values (48) 100481
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 277713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 37697
 
13.6%
l 21828
 
7.9%
20150
 
7.3%
o 18838
 
6.8%
i 16766
 
6.0%
a 15987
 
5.8%
r 14804
 
5.3%
e 11897
 
4.3%
t 9880
 
3.6%
s 9385
 
3.4%
Other values (48) 100481
36.2%
Distinct6425
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-05-12T02:01:49.956502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length306
Median length286
Mean length15.735437
Min length5

Characters and Unicode

Total characters314158
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2874 ?
Unique (%)14.4%

Sample

1st rowBoonsripaisal, S
2nd rowKang, Y
3rd rowWang, Y
4th rowBoonsripaisal, S
5th rowKang, Y
ValueCountFrequency (%)
j 2489
 
4.7%
a 2051
 
3.9%
m 2025
 
3.8%
s 1530
 
2.9%
c 1298
 
2.5%
d 1225
 
2.3%
k 921
 
1.7%
r 907
 
1.7%
l 897
 
1.7%
e 817
 
1.5%
Other values (7136) 38591
73.2%
2025-05-12T02:01:50.530981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32786
 
10.4%
, 30963
 
9.9%
e 20139
 
6.4%
a 19779
 
6.3%
n 15922
 
5.1%
r 15316
 
4.9%
i 13818
 
4.4%
o 13693
 
4.4%
; 11046
 
3.5%
l 10453
 
3.3%
Other values (47) 130243
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 314158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32786
 
10.4%
, 30963
 
9.9%
e 20139
 
6.4%
a 19779
 
6.3%
n 15922
 
5.1%
r 15316
 
4.9%
i 13818
 
4.4%
o 13693
 
4.4%
; 11046
 
3.5%
l 10453
 
3.3%
Other values (47) 130243
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 314158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32786
 
10.4%
, 30963
 
9.9%
e 20139
 
6.4%
a 19779
 
6.3%
n 15922
 
5.1%
r 15316
 
4.9%
i 13818
 
4.4%
o 13693
 
4.4%
; 11046
 
3.5%
l 10453
 
3.3%
Other values (47) 130243
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 314158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32786
 
10.4%
, 30963
 
9.9%
e 20139
 
6.4%
a 19779
 
6.3%
n 15922
 
5.1%
r 15316
 
4.9%
i 13818
 
4.4%
o 13693
 
4.4%
; 11046
 
3.5%
l 10453
 
3.3%
Other values (47) 130243
41.5%
Distinct4036
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-05-12T02:01:50.833056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length15.979314
Min length6

Characters and Unicode

Total characters319027
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique982 ?
Unique (%)4.9%

Sample

1st rowBoonsripaisal, Simon
2nd rowKang, Yoonjung
3rd rowWang, Yu
4th rowBoonsripaisal, Simon
5th rowKang, Yoonjung
ValueCountFrequency (%)
m 1802
 
3.4%
a 1409
 
2.6%
j 1405
 
2.6%
l 1016
 
1.9%
r 726
 
1.4%
c 656
 
1.2%
e 640
 
1.2%
s 632
 
1.2%
d 510
 
1.0%
k 471
 
0.9%
Other values (5052) 44234
82.7%
2025-05-12T02:01:51.252219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33536
 
10.5%
a 27466
 
8.6%
e 24607
 
7.7%
n 21107
 
6.6%
, 19928
 
6.2%
i 18874
 
5.9%
r 17009
 
5.3%
o 13936
 
4.4%
l 12730
 
4.0%
s 9858
 
3.1%
Other values (47) 119976
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 319027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
33536
 
10.5%
a 27466
 
8.6%
e 24607
 
7.7%
n 21107
 
6.6%
, 19928
 
6.2%
i 18874
 
5.9%
r 17009
 
5.3%
o 13936
 
4.4%
l 12730
 
4.0%
s 9858
 
3.1%
Other values (47) 119976
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 319027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
33536
 
10.5%
a 27466
 
8.6%
e 24607
 
7.7%
n 21107
 
6.6%
, 19928
 
6.2%
i 18874
 
5.9%
r 17009
 
5.3%
o 13936
 
4.4%
l 12730
 
4.0%
s 9858
 
3.1%
Other values (47) 119976
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 319027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
33536
 
10.5%
a 27466
 
8.6%
e 24607
 
7.7%
n 21107
 
6.6%
, 19928
 
6.2%
i 18874
 
5.9%
r 17009
 
5.3%
o 13936
 
4.4%
l 12730
 
4.0%
s 9858
 
3.1%
Other values (47) 119976
37.6%
Distinct2516
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-05-12T02:01:51.641473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length586
Median length383
Mean length81.689807
Min length7

Characters and Unicode

Total characters1630937
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique649 ?
Unique (%)3.3%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
or 12635
 
4.7%
hours 11487
 
4.2%
prerequisite 11035
 
4.1%
and 8737
 
3.2%
credit 8236
 
3.0%
of 7379
 
2.7%
graduate 6753
 
2.5%
for 6099
 
2.2%
4 5635
 
2.1%
math 5489
 
2.0%
Other values (1766) 187763
69.2%
2025-05-12T02:01:52.183834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
251389
15.4%
e 136358
 
8.4%
r 119141
 
7.3%
t 91237
 
5.6%
o 91077
 
5.6%
n 81156
 
5.0%
i 79585
 
4.9%
a 72262
 
4.4%
s 65744
 
4.0%
u 57279
 
3.5%
Other values (66) 585709
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1630937
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
251389
15.4%
e 136358
 
8.4%
r 119141
 
7.3%
t 91237
 
5.6%
o 91077
 
5.6%
n 81156
 
5.0%
i 79585
 
4.9%
a 72262
 
4.4%
s 65744
 
4.0%
u 57279
 
3.5%
Other values (66) 585709
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1630937
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
251389
15.4%
e 136358
 
8.4%
r 119141
 
7.3%
t 91237
 
5.6%
o 91077
 
5.6%
n 81156
 
5.0%
i 79585
 
4.9%
a 72262
 
4.4%
s 65744
 
4.0%
u 57279
 
3.5%
Other values (66) 585709
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1630937
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
251389
15.4%
e 136358
 
8.4%
r 119141
 
7.3%
t 91237
 
5.6%
o 91077
 
5.6%
n 81156
 
5.0%
i 79585
 
4.9%
a 72262
 
4.4%
s 65744
 
4.0%
u 57279
 
3.5%
Other values (66) 585709
35.9%
Distinct175
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-05-12T02:01:52.517550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length659
Median length7
Mean length25.225344
Min length7

Characters and Unicode

Total characters503624
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown 16656
22.0%
one 4037
 
5.3%
and 3319
 
4.4%
for 3235
 
4.3%
students 3108
 
4.1%
section 2513
 
3.3%
must 2402
 
3.2%
register 2178
 
2.9%
lecture 2157
 
2.9%
the 1509
 
2.0%
Other values (573) 34566
45.7%
2025-05-12T02:01:53.020988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 78023
15.5%
55746
 
11.1%
e 40790
 
8.1%
o 40771
 
8.1%
t 29155
 
5.8%
s 27595
 
5.5%
i 24770
 
4.9%
r 23992
 
4.8%
w 19497
 
3.9%
k 17597
 
3.5%
Other values (64) 145688
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 503624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 78023
15.5%
55746
 
11.1%
e 40790
 
8.1%
o 40771
 
8.1%
t 29155
 
5.8%
s 27595
 
5.5%
i 24770
 
4.9%
r 23992
 
4.8%
w 19497
 
3.9%
k 17597
 
3.5%
Other values (64) 145688
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 503624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 78023
15.5%
55746
 
11.1%
e 40790
 
8.1%
o 40771
 
8.1%
t 29155
 
5.8%
s 27595
 
5.5%
i 24770
 
4.9%
r 23992
 
4.8%
w 19497
 
3.9%
k 17597
 
3.5%
Other values (64) 145688
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 503624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 78023
15.5%
55746
 
11.1%
e 40790
 
8.1%
o 40771
 
8.1%
t 29155
 
5.8%
s 27595
 
5.5%
i 24770
 
4.9%
r 23992
 
4.8%
w 19497
 
3.9%
k 17597
 
3.5%
Other values (64) 145688
28.9%

average_grade
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4819374
Minimum1.14
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:53.146152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile2.78
Q13.27
median3.55
Q33.77
95-th percentile3.93
Maximum4
Range2.86
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.360474
Coefficient of variation (CV)0.10352685
Kurtosis0.90170787
Mean3.4819374
Median Absolute Deviation (MAD)0.24
Skewness-0.96366978
Sum69516.88
Variance0.12994151
MonotonicityNot monotonic
2025-05-12T02:01:53.292454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.83 331
 
1.7%
3.67 275
 
1.4%
3.86 275
 
1.4%
3.76 272
 
1.4%
3.62 267
 
1.3%
3.91 264
 
1.3%
3.64 264
 
1.3%
3.92 263
 
1.3%
3.79 262
 
1.3%
3.81 251
 
1.3%
Other values (203) 17241
86.4%
ValueCountFrequency (%)
1.14 1
 
< 0.1%
1.21 1
 
< 0.1%
1.41 1
 
< 0.1%
1.64 1
 
< 0.1%
1.75 1
 
< 0.1%
1.76 2
< 0.1%
1.78 3
< 0.1%
1.79 1
 
< 0.1%
1.83 1
 
< 0.1%
1.85 1
 
< 0.1%
ValueCountFrequency (%)
4 2
 
< 0.1%
3.99 26
 
0.1%
3.98 81
 
0.4%
3.97 177
0.9%
3.96 214
1.1%
3.95 191
1.0%
3.94 210
1.1%
3.93 217
1.1%
3.92 263
1.3%
3.91 264
1.3%

a_plus
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.180666
Minimum0
Maximum100
Zeros5025
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:53.429932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q312
95-th percentile42
Maximum100
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation17.257687
Coefficient of variation (CV)1.6951432
Kurtosis12.517179
Mean10.180666
Median Absolute Deviation (MAD)4
Skewness3.3094924
Sum203257
Variance297.82776
MonotonicityNot monotonic
2025-05-12T02:01:53.568237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5025
25.2%
1 1607
 
8.0%
2 1363
 
6.8%
3 1215
 
6.1%
4 1134
 
5.7%
5 950
 
4.8%
6 820
 
4.1%
7 675
 
3.4%
8 593
 
3.0%
9 545
 
2.7%
Other values (91) 6038
30.2%
ValueCountFrequency (%)
0 5025
25.2%
1 1607
 
8.0%
2 1363
 
6.8%
3 1215
 
6.1%
4 1134
 
5.7%
5 950
 
4.8%
6 820
 
4.1%
7 675
 
3.4%
8 593
 
3.0%
9 545
 
2.7%
ValueCountFrequency (%)
100 308
1.5%
99 1
 
< 0.1%
98 5
 
< 0.1%
97 1
 
< 0.1%
96 3
 
< 0.1%
95 4
 
< 0.1%
94 3
 
< 0.1%
93 3
 
< 0.1%
92 2
 
< 0.1%
91 7
 
< 0.1%

a
Real number (ℝ)

Distinct101
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.262509
Minimum0
Maximum100
Zeros162
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:53.712407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median13
Q324
95-th percentile75
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.910858
Coefficient of variation (CV)1.0813497
Kurtosis5.0890498
Mean20.262509
Median Absolute Deviation (MAD)7
Skewness2.2783342
Sum404541
Variance480.08571
MonotonicityNot monotonic
2025-05-12T02:01:53.863023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 998
 
5.0%
8 983
 
4.9%
6 937
 
4.7%
7 927
 
4.6%
9 887
 
4.4%
4 830
 
4.2%
10 827
 
4.1%
3 756
 
3.8%
11 742
 
3.7%
100 702
 
3.5%
Other values (91) 11376
57.0%
ValueCountFrequency (%)
0 162
 
0.8%
1 314
 
1.6%
2 568
2.8%
3 756
3.8%
4 830
4.2%
5 998
5.0%
6 937
4.7%
7 927
4.6%
8 983
4.9%
9 887
4.4%
ValueCountFrequency (%)
100 702
3.5%
99 5
 
< 0.1%
98 12
 
0.1%
97 8
 
< 0.1%
96 9
 
< 0.1%
95 9
 
< 0.1%
94 8
 
< 0.1%
93 12
 
0.1%
92 9
 
< 0.1%
91 10
 
0.1%

a_minus
Real number (ℝ)

High correlation  Zeros 

Distinct96
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6346106
Minimum0
Maximum100
Zeros2912
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:53.999635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile26
Maximum100
Range100
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.661267
Coefficient of variation (CV)1.5274213
Kurtosis25.70976
Mean7.6346106
Median Absolute Deviation (MAD)3
Skewness4.3709658
Sum152425
Variance135.98515
MonotonicityNot monotonic
2025-05-12T02:01:54.129563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2912
14.6%
3 1989
10.0%
2 1833
9.2%
4 1735
 
8.7%
1 1621
 
8.1%
5 1556
 
7.8%
6 1349
 
6.8%
7 1046
 
5.2%
8 876
 
4.4%
9 669
 
3.4%
Other values (86) 4379
21.9%
ValueCountFrequency (%)
0 2912
14.6%
1 1621
8.1%
2 1833
9.2%
3 1989
10.0%
4 1735
8.7%
5 1556
7.8%
6 1349
6.8%
7 1046
 
5.2%
8 876
 
4.4%
9 669
 
3.4%
ValueCountFrequency (%)
100 97
0.5%
99 1
 
< 0.1%
98 2
 
< 0.1%
97 1
 
< 0.1%
95 3
 
< 0.1%
94 1
 
< 0.1%
89 3
 
< 0.1%
88 3
 
< 0.1%
87 1
 
< 0.1%
86 2
 
< 0.1%

b_plus
Real number (ℝ)

High correlation  Zeros 

Distinct95
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3582269
Minimum0
Maximum100
Zeros3848
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:54.272917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile20
Maximum100
Range100
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.9145523
Coefficient of variation (CV)1.6637131
Kurtosis34.691693
Mean5.3582269
Median Absolute Deviation (MAD)2
Skewness4.8785993
Sum106977
Variance79.469243
MonotonicityNot monotonic
2025-05-12T02:01:54.407776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3848
19.3%
1 2992
15.0%
2 2532
12.7%
3 2139
10.7%
4 1624
8.1%
5 1301
 
6.5%
6 932
 
4.7%
7 680
 
3.4%
8 582
 
2.9%
9 437
 
2.2%
Other values (85) 2898
14.5%
ValueCountFrequency (%)
0 3848
19.3%
1 2992
15.0%
2 2532
12.7%
3 2139
10.7%
4 1624
8.1%
5 1301
 
6.5%
6 932
 
4.7%
7 680
 
3.4%
8 582
 
2.9%
9 437
 
2.2%
ValueCountFrequency (%)
100 23
0.1%
99 1
 
< 0.1%
98 2
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
93 2
 
< 0.1%
92 2
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
88 1
 
< 0.1%

b
Real number (ℝ)

High correlation  Zeros 

Distinct94
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4649637
Minimum0
Maximum100
Zeros3085
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:54.558481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile25
Maximum100
Range100
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.233137
Coefficient of variation (CV)1.7375406
Kurtosis22.437944
Mean6.4649637
Median Absolute Deviation (MAD)2
Skewness4.2030618
Sum129073
Variance126.18337
MonotonicityNot monotonic
2025-05-12T02:01:54.693445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3239
16.2%
0 3085
15.5%
2 2593
13.0%
3 2079
10.4%
4 1603
8.0%
5 1269
 
6.4%
6 965
 
4.8%
7 732
 
3.7%
8 569
 
2.8%
9 454
 
2.3%
Other values (84) 3377
16.9%
ValueCountFrequency (%)
0 3085
15.5%
1 3239
16.2%
2 2593
13.0%
3 2079
10.4%
4 1603
8.0%
5 1269
 
6.4%
6 965
 
4.8%
7 732
 
3.7%
8 569
 
2.8%
9 454
 
2.3%
ValueCountFrequency (%)
100 41
0.2%
97 2
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
92 3
 
< 0.1%
91 2
 
< 0.1%
89 1
 
< 0.1%
88 3
 
< 0.1%
87 4
 
< 0.1%
86 2
 
< 0.1%

b_minus
Real number (ℝ)

High correlation  Zeros 

Distinct66
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.674831
Minimum0
Maximum100
Zeros7973
Zeros (%)39.9%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:54.843754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2935233
Coefficient of variation (CV)1.9790123
Kurtosis42.929849
Mean2.674831
Median Absolute Deviation (MAD)1
Skewness5.1560234
Sum53403
Variance28.021389
MonotonicityNot monotonic
2025-05-12T02:01:54.977574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7973
39.9%
1 3809
19.1%
2 2352
 
11.8%
3 1596
 
8.0%
4 1010
 
5.1%
5 694
 
3.5%
6 461
 
2.3%
7 334
 
1.7%
8 276
 
1.4%
9 207
 
1.0%
Other values (56) 1253
 
6.3%
ValueCountFrequency (%)
0 7973
39.9%
1 3809
19.1%
2 2352
 
11.8%
3 1596
 
8.0%
4 1010
 
5.1%
5 694
 
3.5%
6 461
 
2.3%
7 334
 
1.7%
8 276
 
1.4%
9 207
 
1.0%
ValueCountFrequency (%)
100 1
< 0.1%
99 1
< 0.1%
82 2
< 0.1%
75 1
< 0.1%
72 1
< 0.1%
66 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
60 1
< 0.1%
58 2
< 0.1%

c_plus
Real number (ℝ)

High correlation  Zeros 

Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7895317
Minimum0
Maximum69
Zeros10415
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:55.106696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8
Maximum69
Range69
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0909786
Coefficient of variation (CV)2.286061
Kurtosis46.895703
Mean1.7895317
Median Absolute Deviation (MAD)0
Skewness5.6040265
Sum35728
Variance16.736106
MonotonicityNot monotonic
2025-05-12T02:01:55.243546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10415
52.2%
1 3724
 
18.7%
2 1997
 
10.0%
3 1119
 
5.6%
4 666
 
3.3%
5 458
 
2.3%
6 280
 
1.4%
7 226
 
1.1%
8 170
 
0.9%
9 121
 
0.6%
Other values (47) 789
 
4.0%
ValueCountFrequency (%)
0 10415
52.2%
1 3724
 
18.7%
2 1997
 
10.0%
3 1119
 
5.6%
4 666
 
3.3%
5 458
 
2.3%
6 280
 
1.4%
7 226
 
1.1%
8 170
 
0.9%
9 121
 
0.6%
ValueCountFrequency (%)
69 1
 
< 0.1%
63 1
 
< 0.1%
60 2
< 0.1%
59 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
54 2
< 0.1%
53 2
< 0.1%
51 1
 
< 0.1%
50 3
< 0.1%

c
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.35998
Minimum0
Maximum74
Zeros9405
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:55.378847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile11
Maximum74
Range74
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.546419
Coefficient of variation (CV)2.3501975
Kurtosis30.575372
Mean2.35998
Median Absolute Deviation (MAD)1
Skewness4.9559132
Sum47117
Variance30.762764
MonotonicityNot monotonic
2025-05-12T02:01:55.542037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9405
47.1%
1 4016
20.1%
2 2137
 
10.7%
3 1204
 
6.0%
4 714
 
3.6%
5 420
 
2.1%
6 354
 
1.8%
7 233
 
1.2%
8 199
 
1.0%
9 138
 
0.7%
Other values (48) 1145
 
5.7%
ValueCountFrequency (%)
0 9405
47.1%
1 4016
20.1%
2 2137
 
10.7%
3 1204
 
6.0%
4 714
 
3.6%
5 420
 
2.1%
6 354
 
1.8%
7 233
 
1.2%
8 199
 
1.0%
9 138
 
0.7%
ValueCountFrequency (%)
74 1
 
< 0.1%
56 3
 
< 0.1%
55 2
 
< 0.1%
54 1
 
< 0.1%
53 6
< 0.1%
52 1
 
< 0.1%
51 4
 
< 0.1%
50 11
0.1%
49 7
< 0.1%
48 3
 
< 0.1%

c_minus
Real number (ℝ)

High correlation  Zeros 

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99378913
Minimum0
Maximum52
Zeros13268
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:55.682796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum52
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6729988
Coefficient of variation (CV)2.6897042
Kurtosis52.339731
Mean0.99378913
Median Absolute Deviation (MAD)0
Skewness6.0302589
Sum19841
Variance7.1449224
MonotonicityNot monotonic
2025-05-12T02:01:55.821230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 13268
66.5%
1 3250
 
16.3%
2 1355
 
6.8%
3 647
 
3.2%
4 395
 
2.0%
5 243
 
1.2%
6 154
 
0.8%
7 109
 
0.5%
8 95
 
0.5%
9 63
 
0.3%
Other values (28) 386
 
1.9%
ValueCountFrequency (%)
0 13268
66.5%
1 3250
 
16.3%
2 1355
 
6.8%
3 647
 
3.2%
4 395
 
2.0%
5 243
 
1.2%
6 154
 
0.8%
7 109
 
0.5%
8 95
 
0.5%
9 63
 
0.3%
ValueCountFrequency (%)
52 1
 
< 0.1%
38 1
 
< 0.1%
37 3
< 0.1%
36 3
< 0.1%
35 1
 
< 0.1%
34 2
 
< 0.1%
33 6
< 0.1%
32 1
 
< 0.1%
29 3
< 0.1%
28 2
 
< 0.1%

d_plus
Real number (ℝ)

Zeros 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49366391
Minimum0
Maximum34
Zeros15822
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:55.934968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.563561
Coefficient of variation (CV)3.167258
Kurtosis65.257559
Mean0.49366391
Median Absolute Deviation (MAD)0
Skewness6.6076837
Sum9856
Variance2.4447229
MonotonicityNot monotonic
2025-05-12T02:01:56.047439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 15822
79.2%
1 2271
 
11.4%
2 794
 
4.0%
3 404
 
2.0%
4 208
 
1.0%
5 110
 
0.6%
6 80
 
0.4%
7 63
 
0.3%
8 43
 
0.2%
9 38
 
0.2%
Other values (18) 132
 
0.7%
ValueCountFrequency (%)
0 15822
79.2%
1 2271
 
11.4%
2 794
 
4.0%
3 404
 
2.0%
4 208
 
1.0%
5 110
 
0.6%
6 80
 
0.4%
7 63
 
0.3%
8 43
 
0.2%
9 38
 
0.2%
ValueCountFrequency (%)
34 1
 
< 0.1%
30 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 2
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 3
< 0.1%
19 4
< 0.1%
18 2
< 0.1%

d
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6949161
Minimum0
Maximum30
Zeros14264
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:56.160737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8196419
Coefficient of variation (CV)2.6185059
Kurtosis39.649735
Mean0.6949161
Median Absolute Deviation (MAD)0
Skewness5.2791048
Sum13874
Variance3.3110967
MonotonicityNot monotonic
2025-05-12T02:01:56.276556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14264
71.4%
1 3071
 
15.4%
2 1098
 
5.5%
3 537
 
2.7%
4 292
 
1.5%
5 175
 
0.9%
6 128
 
0.6%
7 90
 
0.5%
8 74
 
0.4%
9 58
 
0.3%
Other values (18) 178
 
0.9%
ValueCountFrequency (%)
0 14264
71.4%
1 3071
 
15.4%
2 1098
 
5.5%
3 537
 
2.7%
4 292
 
1.5%
5 175
 
0.9%
6 128
 
0.6%
7 90
 
0.5%
8 74
 
0.4%
9 58
 
0.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
26 1
 
< 0.1%
25 2
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 4
< 0.1%
20 4
< 0.1%
19 2
 
< 0.1%
18 7
< 0.1%

d_minus
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31189582
Minimum0
Maximum19
Zeros16769
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:56.376746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0308025
Coefficient of variation (CV)3.3049578
Kurtosis53.975452
Mean0.31189582
Median Absolute Deviation (MAD)0
Skewness6.1676
Sum6227
Variance1.0625538
MonotonicityNot monotonic
2025-05-12T02:01:56.491846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 16769
84.0%
1 1960
 
9.8%
2 606
 
3.0%
3 253
 
1.3%
4 132
 
0.7%
5 83
 
0.4%
6 49
 
0.2%
8 27
 
0.1%
7 26
 
0.1%
9 16
 
0.1%
Other values (7) 44
 
0.2%
ValueCountFrequency (%)
0 16769
84.0%
1 1960
 
9.8%
2 606
 
3.0%
3 253
 
1.3%
4 132
 
0.7%
5 83
 
0.4%
6 49
 
0.2%
7 26
 
0.1%
8 27
 
0.1%
9 16
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
15 4
 
< 0.1%
14 3
 
< 0.1%
13 4
 
< 0.1%
12 8
 
< 0.1%
11 8
 
< 0.1%
10 16
0.1%
9 16
0.1%
8 27
0.1%
7 26
0.1%

f
Real number (ℝ)

Zeros 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4024543
Minimum0
Maximum75
Zeros11269
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:56.617534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum75
Range75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.4249202
Coefficient of variation (CV)2.4420904
Kurtosis73.908655
Mean1.4024543
Median Absolute Deviation (MAD)0
Skewness6.789768
Sum28000
Variance11.730078
MonotonicityNot monotonic
2025-05-12T02:01:56.757524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11269
56.4%
1 3776
 
18.9%
2 1873
 
9.4%
3 1006
 
5.0%
4 520
 
2.6%
5 376
 
1.9%
6 233
 
1.2%
7 172
 
0.9%
8 143
 
0.7%
9 85
 
0.4%
Other values (43) 512
 
2.6%
ValueCountFrequency (%)
0 11269
56.4%
1 3776
 
18.9%
2 1873
 
9.4%
3 1006
 
5.0%
4 520
 
2.6%
5 376
 
1.9%
6 233
 
1.2%
7 172
 
0.9%
8 143
 
0.7%
9 85
 
0.4%
ValueCountFrequency (%)
75 1
 
< 0.1%
74 1
 
< 0.1%
64 1
 
< 0.1%
59 1
 
< 0.1%
53 1
 
< 0.1%
51 1
 
< 0.1%
50 2
 
< 0.1%
46 1
 
< 0.1%
45 1
 
< 0.1%
43 5
< 0.1%

w
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18241923
Minimum0
Maximum16
Zeros17651
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size156.1 KiB
2025-05-12T02:01:56.883003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68397627
Coefficient of variation (CV)3.7494745
Kurtosis88.199178
Mean0.18241923
Median Absolute Deviation (MAD)0
Skewness7.5203189
Sum3642
Variance0.46782354
MonotonicityNot monotonic
2025-05-12T02:01:56.973657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 17651
88.4%
1 1669
 
8.4%
2 369
 
1.8%
3 134
 
0.7%
4 52
 
0.3%
5 34
 
0.2%
6 18
 
0.1%
8 10
 
0.1%
7 8
 
< 0.1%
9 8
 
< 0.1%
Other values (5) 12
 
0.1%
ValueCountFrequency (%)
0 17651
88.4%
1 1669
 
8.4%
2 369
 
1.8%
3 134
 
0.7%
4 52
 
0.3%
5 34
 
0.2%
6 18
 
0.1%
7 8
 
< 0.1%
8 10
 
0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 2
 
< 0.1%
10 4
 
< 0.1%
9 8
 
< 0.1%
8 10
 
0.1%
7 8
 
< 0.1%
6 18
0.1%
5 34
0.2%

Interactions

2025-05-12T02:01:37.280512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:02.592572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:04.499593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:06.434367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:08.287853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:10.279432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:14.319658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:16.082572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:18.249238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.990311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.751937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:24.741225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.702430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:28.464844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-12T02:01:32.830487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:34.774907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-12T02:01:02.820081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:04.736071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:06.663012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:08.500000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:12.740496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:14.534412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-12T02:01:25.067051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.916737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:28.703284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:30.562087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:32.942674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:34.912383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:37.770627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:02.929895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:04.846830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:06.782276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-12T02:01:28.813970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:30.665869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:33.051097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:35.049078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-12T02:01:03.251145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-12T02:01:25.607979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:27.424632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:29.229919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.089826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:33.508642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:35.679610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:38.329003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:03.469490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:05.386986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:07.296867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.124629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:13.363068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.143776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:16.919075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.046273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:20.815563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:22.774045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:25.708778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:27.524175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:29.334932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.191538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:33.616389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:35.828165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:38.438711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:03.582682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:05.500092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:07.406451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.228123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:13.462232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.248745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:17.020932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.144321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:20.912325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:22.922179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:25.811977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:27.644721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:29.447304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.295847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:33.721885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:35.997223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:38.553983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:03.703801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:05.622181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:07.516385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.334992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:13.568691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.352354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:17.125654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.263809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.014436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:23.070032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:25.915603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:27.747502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:29.559139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.402566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:33.857114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:36.151958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:38.662939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:03.813646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:05.756452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:07.619610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.437905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:13.671022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.456646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:17.247662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.369104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.112596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:23.224670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.026686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:27.849588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:29.676696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.508370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:33.973506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:36.303685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:38.773631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:03.918965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:05.864266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:07.730832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.542328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:13.766483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.561620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:17.344012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.475477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.218163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:23.385544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.130150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:27.942709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:29.787080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.611294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:34.086624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:36.448439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:38.886502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:04.030241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:05.979714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:07.850539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.652544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:13.867174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.668864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:17.458481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.574867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.330722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:23.549206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.245018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:28.043501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:29.892624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.716542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:34.208289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:36.607654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:38.997519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:04.146259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:06.087209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:07.949715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.785613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:13.978179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.772751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:17.914463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.672336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.438375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:23.694850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.356736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:28.140212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:30.000494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.831448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:34.320757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:36.768811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:39.126157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:04.262538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:06.212583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:08.059419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:09.962742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:14.106298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.876939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:18.018179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.783299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.547049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:23.851029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.472980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:28.252589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:30.109296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:31.940985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:34.438802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:36.938748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:39.246493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:04.386323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:06.320499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:08.171931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:10.111958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:14.214459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:15.979408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:18.116815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:19.885931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:21.649826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:23.994362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:26.591186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:28.353014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:30.222968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:32.578672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:34.547477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T02:01:37.102888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-12T02:01:57.091322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
aa_minusa_plusaverage_gradebb_minusb_pluscc_minusc_pluscourse_numbercrndd_minusd_plusdays_of_weekenrollment_statusfpart_of_termsection_credit_hourssection_statussection_typestart_timetermwyear
a1.0000.203-0.0010.3060.2590.0410.1140.2030.0620.0490.0470.0670.1960.0710.0910.1240.0450.1670.1240.0240.0000.1210.1140.0140.1320.013
a_minus0.2031.0000.189-0.2280.3580.5240.6620.2040.3590.4190.040-0.0670.1570.2420.2870.0970.0460.1990.0370.0420.0000.1070.0780.0060.1510.000
a_plus-0.0010.1891.0000.1440.0100.1840.1570.0830.1880.191-0.1340.0300.1000.1790.2010.1040.0280.1900.1100.0380.0120.0860.0970.0320.1130.013
average_grade0.306-0.2280.1441.000-0.550-0.541-0.415-0.566-0.500-0.5420.1100.196-0.449-0.367-0.4110.1140.093-0.4800.0730.0560.0000.0850.0770.042-0.1640.054
b0.2590.3580.010-0.5501.0000.4950.4940.5860.3830.4740.015-0.1520.4330.2640.3290.1400.0580.3250.1660.0740.0000.1190.0790.0350.2210.014
b_minus0.0410.5240.184-0.5410.4951.0000.5980.4310.5200.594-0.046-0.1410.3150.3440.4130.0530.0420.3190.0000.0130.0000.0850.0200.0130.2060.009
b_plus0.1140.6620.157-0.4150.4940.5981.0000.3190.4110.5450.061-0.1250.2310.2740.3580.0950.0480.2300.0240.0400.0000.0980.0680.0000.1850.012
c0.2030.2040.083-0.5660.5860.4310.3191.0000.4510.473-0.110-0.1240.5020.3320.3980.1240.0550.4090.1200.0970.0000.1130.0560.0090.2280.023
c_minus0.0620.3590.188-0.5000.3830.5200.4110.4511.0000.525-0.145-0.1100.3800.3920.4540.0560.0530.3830.0120.0110.0000.0900.0150.0060.2220.009
c_plus0.0490.4190.191-0.5420.4740.5940.5450.4730.5251.000-0.097-0.1300.3560.3750.4630.0590.0510.3520.0000.0100.0000.0960.0190.0200.2130.003
course_number0.0470.040-0.1340.1100.015-0.0460.061-0.110-0.145-0.0971.0000.131-0.124-0.151-0.1650.3010.208-0.2450.1480.2110.0000.1650.2070.086-0.0510.009
crn0.067-0.0670.0300.196-0.152-0.141-0.125-0.124-0.110-0.1300.1311.000-0.074-0.087-0.0900.0900.063-0.0240.1450.0670.0000.1650.0920.169-0.0130.080
d0.1960.1570.100-0.4490.4330.3150.2310.5020.3800.356-0.124-0.0741.0000.3380.3790.0620.0410.4020.1090.0250.0000.0740.0280.0190.2320.025
d_minus0.0710.2420.179-0.3670.2640.3440.2740.3320.3920.375-0.151-0.0870.3381.0000.4310.0500.0500.3290.0180.0150.0000.0790.0190.0000.2150.030
d_plus0.0910.2870.201-0.4110.3290.4130.3580.3980.4540.463-0.165-0.0900.3790.4311.0000.0320.0530.3640.0000.0120.0000.0780.0000.0160.2160.018
days_of_week0.1240.0970.1040.1140.1400.0530.0950.1240.0560.0590.3010.0900.0620.0500.0321.0000.1240.0750.3230.1680.0000.2260.2790.0950.0340.065
enrollment_status0.0450.0460.0280.0930.0580.0420.0480.0550.0530.0510.2080.0630.0410.0500.0530.1241.0000.0350.0360.1710.0230.1630.1000.0450.0310.354
f0.1670.1990.190-0.4800.3250.3190.2300.4090.3830.352-0.245-0.0240.4020.3290.3640.0750.0351.0000.2100.0000.0000.0610.0700.0000.2530.024
part_of_term0.1240.0370.1100.0730.1660.0000.0240.1200.0120.0000.1480.1450.1090.0180.0000.3230.0360.2101.0000.1860.0220.2730.3280.0240.0240.000
section_credit_hours0.0240.0420.0380.0560.0740.0130.0400.0970.0110.0100.2110.0670.0250.0150.0120.1680.1710.0000.1861.0000.0120.1110.0940.0590.0010.013
section_status0.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0220.0121.0000.0000.0000.0000.0000.000
section_type0.1210.1070.0860.0850.1190.0850.0980.1130.0900.0960.1650.1650.0740.0790.0780.2260.1630.0610.2730.1110.0001.0000.1920.0690.0470.251
start_time0.1140.0780.0970.0770.0790.0200.0680.0560.0150.0190.2070.0920.0280.0190.0000.2790.1000.0700.3280.0940.0000.1921.0000.0420.0220.062
term0.0140.0060.0320.0420.0350.0130.0000.0090.0060.0200.0860.1690.0190.0000.0160.0950.0450.0000.0240.0590.0000.0690.0421.0000.0390.022
w0.1320.1510.113-0.1640.2210.2060.1850.2280.2220.213-0.051-0.0130.2320.2150.2160.0340.0310.2530.0240.0010.0000.0470.0220.0391.0000.059
year0.0130.0000.0130.0540.0140.0090.0120.0230.0090.0030.0090.0800.0250.0300.0180.0650.3540.0240.0000.0130.0000.2510.0620.0220.0591.000

Missing values

2025-05-12T02:01:39.513401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-12T02:01:39.869491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-12T02:01:40.166623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yeartermcrnsubject_sectioncourse_numbercourse_titledescriptionpart_of_termdegree_attributescredit_hourssection_credit_hourssection_statussection_typesection_type_codeenrollment_statusstart_timeend_timedays_of_weekroombuildinginstructors_abbrinstructors_fnsection_infoschedule_informationaverage_gradea_plusaa_minusb_plusbb_minusc_pluscc_minusd_plusdd_minusfw
02020FALL41758AD1100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen09:00 AM09:50 AMFUnknownUnknownBoonsripaisal, SBoonsripaisal, SimonUnknownUnknown3.55109402000101010
12020FALL47100AD2100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen12:00 PM12:50 PMFUnknownUnknownKang, YKang, YoonjungUnknownUnknown3.28211225102001010
22020FALL47102AD3100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen01:00 PM01:50 PMFUnknownUnknownWang, YWang, YuUnknownUnknown3.44116330002000020
32020FALL51248AD4100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen10:00 AM10:50 AMFUnknownUnknownBoonsripaisal, SBoonsripaisal, SimonUnknownUnknown3.76158100211000000
42020FALL51249AD5100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODClosed11:00 AM11:50 AMFUnknownUnknownKang, YKang, YoonjungUnknownUnknown3.52414431101010010
52020FALL51932AD6100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen02:00 PM02:50 PMFUnknownUnknownWang, YWang, YuUnknownUnknown3.31115033131010010
62020FALL59818AD7100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen10:00 AM10:50 AMFUnknownUnknownGuruparan, AGuruparan, AkilUnknownUnknown3.43711223011000020
72020FALL59819AD8100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen11:00 AM11:50 AMFUnknownUnknownGuruparan, AGuruparan, AkilUnknownUnknown3.49412431011100010
82020FALL59820AD9100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen02:00 PM02:50 PMFUnknownUnknownHorton, CHorton, Christina MUnknownUnknown3.63214325000010000
92020FALL59821ADA100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.1.0Social & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaNUnknownAOnline DiscussionODOpen01:00 PM01:50 PMFUnknownUnknownHorton, CHorton, Christina MUnknownUnknown3.83714501001000000
yeartermcrnsubject_sectioncourse_numbercourse_titledescriptionpart_of_termdegree_attributescredit_hourssection_credit_hourssection_statussection_typesection_type_codeenrollment_statusstart_timeend_timedays_of_weekroombuildinginstructors_abbrinstructors_fnsection_infoschedule_informationaverage_gradea_plusaa_minusb_plusbb_minusc_pluscc_minusd_plusdd_minusfw
199552023SPRING55158AL1608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.2.0UnknownNaNUnknownALectureLECOpen (Restricted)08:00 AM08:50 AMWUnknownUnknownBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, MVieson, MirandaNo graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.Unknown2.8903600520039006000
199562023SPRING55158AL1608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.2.0UnknownNaNUnknownALectureLECOpen (Restricted)08:00 AM11:50 AMMFUnknownUnknownBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, MVieson, MirandaNo graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.Unknown2.8903600520039006000
199572023SPRING55158AL1608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.2.0UnknownNaNUnknownALectureLECOpen (Restricted)09:00 AM11:50 AMTRUnknownUnknownBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, MVieson, MirandaNo graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.Unknown2.8903600520039006000
199582023SPRING55158AL1608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.2.0UnknownNaNUnknownALectureLECOpen (Restricted)01:00 PM02:50 PMWUnknownUnknownBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, MVieson, MirandaNo graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.Unknown2.8903600520039006000
199592023SPRING72913AL1655SA Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.1.0UnknownNaNUnknownALaboratoryLABOpen08:00 AM11:50 AMTUnknownUnknownCappa, T;Elston, C;Foreman, J;Foss, K;Hague, DRidgway, Marcella DNo graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.Unknown2.9903300680022006000
199602023SPRING72913AL1655SA Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.1.0UnknownNaNUnknownALectureLECOpen08:00 AM10:50 AMFUnknownUnknownAldridge, R;Fan, T;Fick, M;Foreman, J;Foss, K;Gal, A;Garrett, L;Gleason, H;Hague, D;Haraschak, J;Keller, K;Labelle, A;Lundberg, A;McCoy, A;Moran, C;Ridgway, M;Sander, S;Selting, K;Welle, K;Wismer, TRidgway, Marcella DNo graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.Unknown2.9903300680022006000
199612023SPRING72913AL1655SA Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.1.0UnknownNaNUnknownALectureLECOpen09:00 AM10:50 AMMWUnknownUnknownAldridge, R;Fan, T;Fick, M;Foreman, J;Foss, K;Gal, A;Garrett, L;Gleason, H;Hague, D;Haraschak, J;Keller, K;Labelle, A;Lundberg, A;McCoy, A;Moran, C;Ridgway, M;Sander, S;Selting, K;Welle, K;Wismer, TRidgway, Marcella DNo graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.Unknown2.9903300680022006000
199622023SPRING72913AL1655SA Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.1.0UnknownNaNUnknownALectureLECOpen01:00 PM01:50 PMMTWRFUnknownUnknownAldridge, R;Fan, T;Fick, M;Foreman, J;Foss, K;Gal, A;Garrett, L;Gleason, H;Hague, D;Haraschak, J;Keller, K;Labelle, A;Lundberg, A;McCoy, A;Moran, C;Ridgway, M;Sander, S;Selting, K;Welle, K;Wismer, TRidgway, Marcella DNo graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.Unknown2.9903300680022006000
199632023SPRING72914AL1656LA Medicine and Surgery IIITeaches the practice of Large Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 655. No graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.1.0UnknownNaNUnknownAPackaged SectionPKGOpen11:00 AM11:50 AMMWFUnknownUnknownAldridge, R;Austin, S;Foreman, J;French, D;Garrett, E;Gutierrez Nibeyro, S;Lowe, J;McCoy, A;Stewart, M;Wilkins, PGarrett, Edgar FNo graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.Unknown2.8503000530043003000
199642023SPRING72914AL1656LA Medicine and Surgery IIITeaches the practice of Large Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 655. No graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.1.0UnknownNaNUnknownAPackaged SectionPKGOpen02:00 PM02:50 PMMTWRFUnknownUnknownAldridge, R;Austin, S;Foreman, J;French, D;Garrett, E;Gutierrez Nibeyro, S;Lowe, J;McCoy, A;Stewart, M;Wilkins, PGarrett, Edgar FNo graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.Unknown2.8503000530043003000